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High resolution annotation of gene functions is a central goal in functional genomics. A single gene may produce multiple isoforms with different functions through alternative splicing. Conventional approaches, however, consider a gene as a single entity without differentiating these functionally different isoforms. Towards understanding gene functions at higher resolution, recent efforts have focused on predicting the functions of isoforms. However, the performance of existing methods is far from satisfactory mainly because of the lack of isoform-level functional annotation. We present IsoResolve, a novel approach for isoform function prediction, which leverages the information from gene function prediction models with domain adaptation (DA). IsoResolve treats gene-level and isoform-level features as source and target domains, respectively. It uses DA to project the two domains into a latent variable space in such a way that the latent variables from the two domains have similar distribution, which enables the gene domain information to be leveraged for isoform function prediction. We systematically evaluated the performance of IsoResolve in predicting functions. Compared with five state-of-the-art methods, IsoResolve achieved significantly better performance. IsoResolve was further validated by case studies of genes with isoform-level functional annotation. IsoResolve is freely available at Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:


Hong-Dong Li, Changhuo Yang, Zhimin Zhang, Mengyun Yang, Fang-Xiang Wu, Gilbert S Omenn, Jianxin Wang. IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation. Bioinformatics (Oxford, England). 2021 May 01;37(4):522-530

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PMID: 32966552

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